AI Sentiment Analysis-Based Brand Reputation Management Strategy in Responding to Social and Ethical Issues on Social Media
Keywords:
ai sentiment analysis, natural language processing, reputation management, crisis communication, social media, ethical issues, brand image, public trustAbstract
The dynamic evolution of social media has heightened brand reputation crisis vulnerability due to intense public scrutiny over social and ethical business issues. This study aims to analyze the utilization of Natural Language Processing (NLP)-based AI Sentiment Analysis in mapping reputation crises, formulating responsive corporate crisis communication strategies, and evaluating its impact on brand image recovery and public trust. An exploratory qualitative approach using a case study design was conducted through social media listening and in-depth interviews with crisis communication practitioners and data analysts. The findings demonstrate that AI Sentiment Analysis precisely detects netizen negative emotion anomalies, maps key actor network clusters, and categorizes social issue topics in real-time. Formulating crisis communication strategies that balance analytical data-driven response speed with empathetic messaging significantly reduces crisis escalation duration, restores corporate social legitimacy, and strengthens customer loyalty. This study concludes that integrating artificial intelligence with transparent communication governance forms the core foundation for successful modern corporate reputation risk mitigation.
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